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Archive of posts filed under the Stan category.

When MCMC fails: The advice we’re giving is wrong. Here’s what we you should be doing instead. (Hint: it’s all about the folk theorem.)

In applied Bayesian statistics we often use Markov chain Monte Carlo: a family of iterative algorithms that yield approximate draws from the posterior distribution. For example, Stan uses Hamiltonian Monte Carlo. One annoying thing about these iterative algorithms is that they can take awhile, but on the plus side this spins off all sorts of […]

Network of models

Ryan Bernstein shows this demo of a prototype of the network of models visualization in Stan. This is related to the topology of models, an idea that we’ve discussed on occasion and is a key part of statistical workflow that I don’t think is handled well by existing theory or software. What Ryan is doing […]

Short course on football (soccer) analytics using Stan

From Ioannis Ntzoufras, Dimitrios Karlis, and Leonardo Egidi. I haven’t looked at the course myself but I like the idea!

Postdoc position in Bayesian modeling for cancer

Wesley Tansey writes: I’m recruiting a postdoc to join my lab at Memorial Sloan Kettering Cancer Center ( The role overlaps a lot with the interests of people on your blog. We’re specifically looking for people with experience in subset of the following: – Bayesian hierarchical models – Spatial statistical methods (e.g. Gaussian processes, trend […]

Whatever you’re looking for, it’s somewhere in the Stan documentation and you can just google for it.

Someone writes: Do you have link to an example of Zero-inflated poisson and Zero-inflated negbin model using pure stan (not brms, nor rstanarm)? If yes, please share it with me! I had a feeling there was something in the existing documentation already! So I googled *zero inflated Stan*, and . . . yup, it’s the […]

Hierarchical modeling of excess mortality time series

Elliott writes: My boss asks me: For our model to predict excess mortality around the world, we want to calculate a confidence interval around our mean estimate for total global excess deaths. We have real excess deaths for like 60 countries, and are predicting on another 130 or so. we can easily calculate intervals for […]

Webinar: An introduction to Bayesian multilevel modeling with brms

This post is by Eric. This Wednesday, at 12 pm ET, Paul Bürkner is stopping by to talk to us about brms. You can register here. Abstract The talk will be about Bayesian multilevel models and their implementation in R using the package brms. We will start with a short introduction to multilevel modeling and to […]

Some issues when using MRP to model attitudes on a gun control attitude question on a 1–4 scale

Elliott Morris writes: – I want to run a MRP model predicting 4 categories of response options to a question about gun control (multinomial logit) – I want to control for demographics in the standard hierarchical way (MRP) – I want the coefficients to evolve in a random walk over time, as I have data […]

StanConnect 2021: Call for Session Proposals

Back in February it was decided that this year’s StanCon would be a series of virtual mini-symposia with different organizers instead of a single all-day event. Today the Stan Governing Body (SGB) announced that submissions are now open for anyone to propose organizing a session. Here’s the announcement from the SGB on the Stan forums:  […]

Discuss our new R-hat paper for the journal Bayesian Analysis!

Here’s your opportunity: We welcome public contributions to the Discussion of the manuscript the manuscript Rank-normalization, folding, and localization: An improved R-hat for assessing convergence of MCMC by A. Vehtari, A. Gelman, D. Simpson, B. Carpenter and P. C. Bürkner, which will be featured as a Discussion Paper in the June 2021 issue of the […]

The Folk Theorem, revisited

It’s time to review the folk theorem, an old saw on this blog, on the Stan forums, and in all of Andrew’s and my applied modeling. Folk Theorem Andrew uses “folk” in the sense of being folksy as opposed to rigorous. The Folk Theorem of Statistical Computing (Gelman 2008): When you have computational problems, often […]

Work on Stan as part of Google’s Summer of Code!

The Stan project is excited to announce that we will be participating in Google Summer of Code (GSoC) 2021 as a mentoring organization under the NumFOCUS umbrella. GSoC is an initiative that connects students with open source projects to give them hands-on experience working on open source code. We are thrilled to offer three projects […]

PhD student and postdoc positions in Norway for doing Bayesian causal inference using Stan!

Guido Biele writes: I have two positions for a postdoc and PhD student open in a project where we will use observational data from Norwegian National registries, structural models (or the potential outcomes framework, the main thing is that we want to think systematically about identification), and Bayesian estimation in Stan to estimate causal effects […]

Webinar: On Bayesian workflow

This post is by Eric. This Wednesday, at 12 pm ET, Aki Vehtari is stopping by to talk to us about Bayesian workflow. You can register here. Abstract We will discuss some parts of the Bayesian workflow with a focus on the need and justification for an iterative process. The talk is partly based on […]

Summer research jobs at Flatiron Institute

If you’re an undergrad or grad student and work in applied math, stats, or machine learning, you may be interested in our summer research assistant and associate positions at the Flatiron Institute’s Center for Computational Mathematics: Scientific computing summer positions Machine learning and statistics summer positions There is no deadline, but we’ll start reviewing applications […]

The Mets are hiring

Des McGowan writes: We are looking to hire multiple full time analysts/senior analysts to join the Baseball Analytics department at the New York Mets. The roles will involve building, testing, and presenting statistical models that inform decision-making in all facets of Baseball Operations. These positions require a strong background in complex statistics and data analytics, […]

Postdoc in precision medicine at Johns Hopkins using Bayesian methods

Aki Nishimura writes: My colleague Scott Zeger and I have a postdoc position for our precision medicine initiative at Johns Hopkins and we are looking for expertise in Bayesian methods, statistical computation, or software development. Expertise in Stan would be a plus!

Statisticians don’t use statistical evidence to decide what statistical methods to use. Also, The Way of the Physicist.

David Bailey, a physicist at the University of Toronto, writes: I thought you’d be pleased to hear that a student in our Advanced Physics Lab spontaneously used Stan to analyze data with significant uncertainties in both x and y. We’d normally expect students to use python and orthogonal distance regression, and STAN is never mentioned […]

Postdoc in Paris for Bayesian models in genetics . . . differential equation models in Stan

Julie Bertrand writes: The BIPID team in the IAME UMR1137 INSERM Université de Paris is opening a one-year postdoctoral position to develop Bayesian approaches to high throughput genetic analyses using nonlinear mixed effect models. The candidate will analyse longitudinal phenotype data using differential equation models on clinical trial data with Stan and perform simulation studies […]

Webinar: Some Outstanding Challenges when Solving ODEs in a Bayesian context

This post is by Eric. This Wednesday, at 12 pm ET, Charles Margossian is stopping by to talk to us about solving ODEs using Bayesian methods. You can register here. If you want to get a feel for the types of issues he will be discussing, take a look at his (and Andrew’s) recent case […]

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